Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
408-04-010 ISMRM Abstract

UBNAno: Physics-Informed Brain Lesion Synthesis for Generalizable Anomaly Detection

Accepted
Eunate Alzaga Goñi 1, Rhea Adams1,2, Shahrzad Moinian3, Walter Zhao4, Pew-Thian Yap5, Evan Calabrese6, Dan Ma1,3
1Department of Biomedical Engineering, Duke University, Durham, United States of America
2Department of Biomedical Engineering, Case Western Reserve University, Cleveland, United States of America
3Department of Neurosurgery, Duke University School of Medicine, Durham, United States of America
4Medical Scientist Training Program, Case Western Reserve University School of Medicine, Cleveland, United States of America
5Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, United States of America
6Department of Radiology, Duke University Medical Center, Durham, United States of America
Presenting Author: Eunate Alzaga Goñi

Synopsis

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References

1. Wang, S., Li, C., Wang, R., Liu, Z., Wang, M., Tan, H., Wu, Y., Liu, X., Sun, H., Yang, R., Liu, X., Chen, J., Zhou, H., Ayed, I. B., & Zheng, H. (2021). Annotation-efficient deep learning for automatic medical image segmentation. Nature Communications, 12(1). https://doi.org/10.1038/s41467-021-26216-9 [doi]
2. Huang, S., Pareek, A., Jensen, M., Lungren, M. P., Yeung, S., & Chaudhari, A. S. (2023). Self-supervised learning for medical image classification: a systematic review and implementation guidelines. Npj Digital Medicine, 6(1). https://doi.org/10.1038/s41746-023-00811-0 [doi]
3. Adams, R., Zhao, W., Hu, S., Lyu, W., Huynh, K. M., Ahmad, S., Ma, D., & Yap, P. (2024c). UltimateSynth: MRI Physics for Pan-Contrast AI. bioRxiv (Cold Spring Harbor Laboratory). https://doi.org/10.1101/2024.12.05.627056 [doi]
4. Ma, D., Gulani, V., Seiberlich, N., Liu, K., Sunshine, J. L., Duerk, J. L., & Griswold, M. A. (2013). Magnetic resonance fingerprinting. Nature, 495(7440), 187–192. https://doi.org/10.1038/nature11971 [doi]
5. Englund, E., Brun, A., Larsson, E., Györffy-Wagner, Z., & Persson, B. (1986). Tumours of the central nervous system. Acta Radiologica Diagnosis, 27(6), 653–659. https://doi.org/10.1177/028418518602700606https://doi.org/10.1007/s00415-017-8609-6 [doi]
6. Laule, C., Bjarnason, T. A., Vavasour, I. M., Traboulsee, A. L., Moore, G. R. W., Li, D. K. B., & MacKay, A. L. (2017). Characterization of brain tumours with spin–spin relaxation: pilot case study reveals unique T 2 distribution profiles of glioblastoma, oligodendroglioma and meningioma. Journal of Neurology, 264(11), 2205–2214. https://doi.org/10.1007/s00415-017-8609-6 [doi]
7. Badve, C., Yu, A., Dastmalchian, S., Rogers, M., Ma, D., Jiang, Y., Margevicius, S., Pahwa, S., Lu, Z., Schluchter, M., Sunshine, J., Griswold, M., Sloan, A., & Gulani, V. (2016b). MR fingerprinting of adult Brain Tumors: Initial experience. American Journal of Neuroradiology, 38(3), 492–499. https://doi.org/10.3174/ajnr.a5035 [doi]
8. Isensee, F., Jaeger, P. F., Kohl, S. a. A., Petersen, J., & Maier-Hein, K. H. (2020). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature Methods, 18(2), 203–211. https://doi.org/10.1038/s41592-020-01008-z [doi]
9. Cepeda, S., García-García, S., Arrese, I., Herrero, F., Escudero, T., Zamora, T., & Sarabia, R. (2023). The Río Hortega University Hospital Glioblastoma dataset: A comprehensive collection of preoperative, early postoperative and recurrence MRI scans (RHUH-GBM). Data in Brief, 50, 109617. https://doi.org/10.1016/j.dib.2023.109617 [doi]
10. Calabrese, E., Villanueva-Meyer, J. E., Rudie, J. D., Rauschecker, A. M., Baid, U., Bakas, S., Cha, S., Mongan, J. T., & Hess, C. P. (2022). The University of California San Francisco Preoperative Diffuse Glioma MRI dataset. Radiology Artificial Intelligence, 4(6). https://doi.org/10.1148/ryai.220058 [doi]
11. Bakas, S., Sako, C., Akbari, H., Bilello, M., Sotiras, A., Shukla, G., Rudie, J. D., Santamaría, N. F., Kazerooni, A. F., Pati, S., Rathore, S., Mamourian, E., Ha, S. M., Parker, W., Doshi, J., Baid, U., Bergman, M., Binder, Z. A., Verma, R., . . . Davatzikos, C. (2022). The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, & radiomics. Scientific Data, 9(1). https://doi.org/10.1038/s41597-022-01560-7 [doi]
12. Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J. S., Freymann, J. B., Farahani, K., & Davatzikos, C. (2017b). Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features. Scientific Data, 4(1). https://doi.org/10.1038/sdata.2017.117 [doi]
13. Calabrese, E. & LaBella, D. (2023). BraTS Meningioma Dataset. Synapse. https://doi.org/10.7303/syn51514106 [doi]
14. Ramakrishnan, D., Jekel, L., Chadha, S., Janas, A., Moy, H., Maleki, N., Sala, M., Kaur, M., Petersen, G. C., Merkaj, S., Von Reppert, M., Baid, U., Bakas, S., Kirsch, C., Davis, M., Bousabarah, K., Holler, W., Lin, M., Westerhoff, M., . . . Aboian, M. S. (2024). A large open access dataset of brain metastasis 3D segmentations on MRI with clinical and imaging information. Scientific Data, 11(1). https://doi.org/10.1038/s41597-024-03021-9 [doi]
15. Luo, G., Xie, W., Gao, R., Zheng, T., Chen, L., & Sun, H. (2023). Unsupervised anomaly detection in brain MRI: Learning abstract distribution from massive healthy brains. Computers in Biology and Medicine, 154, 106610. https://doi.org/10.1016/j.compbiomed.2023.106610 [doi]

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